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- # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- import paddle
- from paddle import nn
- import paddle.nn.functional as F
- from paddlex.paddleseg.cvlibs import manager
- from paddlex.paddleseg.models import losses
- @manager.LOSSES.add_component
- class OhemEdgeAttentionLoss(nn.Layer):
- """
- Implements the cross entropy loss function. It only compute the edge part.
- Args:
- edge_threshold (float, optional): The pixels greater edge_threshold as edges. Default: 0.8.
- thresh (float, optional): The threshold of ohem. Default: 0.7.
- min_kept (int, optional): The min number to keep in loss computation. Default: 5000.
- ignore_index (int64, optional): Specifies a target value that is ignored
- and does not contribute to the input gradient. Default ``255``.
- """
- def __init__(self,
- edge_threshold=0.8,
- thresh=0.7,
- min_kept=5000,
- ignore_index=255):
- super().__init__()
- self.edge_threshold = edge_threshold
- self.thresh = thresh
- self.min_kept = min_kept
- self.ignore_index = ignore_index
- self.EPS = 1e-10
- def forward(self, logits, label):
- """
- Forward computation.
- Args:
- logits (tuple|list): (seg_logit, edge_logit) Tensor, the data type is float32, float64. Shape is
- (N, C), where C is number of classes, and if shape is more than 2D, this
- is (N, C, D1, D2,..., Dk), k >= 1. C =1 of edge_logit .
- label (Tensor): Label tensor, the data type is int64. Shape is (N, C), where each
- value is 0 <= label[i] <= C-1, and if shape is more than 2D, this is
- (N, C, D1, D2,..., Dk), k >= 1.
- """
- seg_logit, edge_logit = logits[0], logits[1]
- if len(label.shape) != len(seg_logit.shape):
- label = paddle.unsqueeze(label, 1)
- if edge_logit.shape != label.shape:
- raise ValueError(
- 'The shape of edge_logit should equal to the label, but they are {} != {}'
- .format(edge_logit.shape, label.shape))
- # Filter out edge
- filler = paddle.ones_like(label) * self.ignore_index
- label = paddle.where(edge_logit > self.edge_threshold, label, filler)
- # ohem
- n, c, h, w = seg_logit.shape
- label = label.reshape((-1, ))
- valid_mask = (label != self.ignore_index).astype('int64')
- num_valid = valid_mask.sum()
- label = label * valid_mask
- prob = F.softmax(seg_logit, axis=1)
- prob = prob.transpose((1, 0, 2, 3)).reshape((c, -1))
- if self.min_kept < num_valid and num_valid > 0:
- # let the value which ignored greater than 1
- prob = prob + (1 - valid_mask)
- # get the prob of relevant label
- label_onehot = F.one_hot(label, c)
- label_onehot = label_onehot.transpose((1, 0))
- prob = prob * label_onehot
- prob = paddle.sum(prob, axis=0)
- threshold = self.thresh
- if self.min_kept > 0:
- index = prob.argsort()
- threshold_index = index[min(len(index), self.min_kept) - 1]
- threshold_index = int(threshold_index.numpy()[0])
- if prob[threshold_index] > self.thresh:
- threshold = prob[threshold_index]
- kept_mask = (prob < threshold).astype('int64')
- label = label * kept_mask
- valid_mask = valid_mask * kept_mask
- # make the invalid region as ignore
- label = label + (1 - valid_mask) * self.ignore_index
- label = label.reshape((n, 1, h, w))
- valid_mask = valid_mask.reshape((n, 1, h, w)).astype('float32')
- loss = F.softmax_with_cross_entropy(
- seg_logit, label, ignore_index=self.ignore_index, axis=1)
- loss = loss * valid_mask
- avg_loss = paddle.mean(loss) / (paddle.mean(valid_mask) + self.EPS)
- label.stop_gradient = True
- valid_mask.stop_gradient = True
- return avg_loss
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